Chemical Profiling, Antibacterial Efficacy, and Synergistic Actions of <i>Ptychotis verticillata</i> Duby Essential Oil in Combination with Conventional Antibiotics
Bibliographic record
Abstract
Introduction: The primary aim of the current investigation is to assess the chemical composition and antibacterial efficacy of Ptychotis verticillata essential oil (PVEO) against three Gram-positive bacteria ( Staphylococcus aureus, Micrococcus luteus, and Bacillus subtilis) and three Gram-negative bacteria ( Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae). Furthermore, this research endeavors to investigate the potential synergistic effects of PVEO when combined with established antibiotics (Amoxicillin, Erythromycin, and Ampicillin), with a focus on discerning their interaction dynamics. Methods: The phytochemical profiling of PVEO was performed via the Gas Chromatography-Mass Spectrometry technique. The broth dilution test determined the interaction effect between PVEO and these three antibiotics. Results: Components γ-terpinene (38.96%), p-cymene (19.38%), thymol (18.07%), and carvacrol (6.99%) are the major bioactive moleculs composing this studied essential oil. PVEO demonstrated significant antibacterial activity with minimum inhibitory concentration (MIC) values ranging from 0.2 against E coli to 2 mg/mL against M luteus. The assessment of the synergistic activity between PVEO and antibiotics was accomplished through the utilization of the Fractional Inhibitory Concentration Index (FICI). The combination of PVEO and amoxicillin against M luteus demonstrated the best synergistic action with a FICI value of 0.43. Conclusion: Significant reductions, ranging from two to sevenfold, in the MIC values of PVEO, and antibiotics were observed. This noteworthy synergistic interaction between highly potent essential oils and synthetic antibiotics holds the potential to open avenues for novel combination therapies aimed at addressing infections caused by multiresistant microorganisms, even at notably reduced concentrations within the pharmaceutical sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".